Modern aircraft are increasingly complex. The complexities of these aircraft have led to an increasing need for automated fault detection systems. These fault detection systems are designed to monitor the various systems of the aircraft in an effort to detect potential faults. These systems are designed to detect these potential faults such that the potential faults can be addressed before the potential faults lead to serious system failure and possible in-flight shutdowns, take-off aborts, delays or cancellations.
Engines are, of course, a particularly critical part of the aircraft. As such, fault detection for aircraft engines are an important part of an aircraft's fault detection system. Traditional engine fault detection in turbine engines has suffered from significant limitations in terms of the type of data they are designed to make use of and their performance. For example, many of the previous approaches have been developed and tested using models. Typically, these models produce data that is well behaved and sampled at regular intervals. Engine data is then compared to the models as part of a fault detection system.
Unfortunately, in many actual field applications, the engine data is neither well behaved nor measured at regular intervals. Noisy data measured at uneven intervals can be present in the systems when engine data is collected manually by the pilot or in automatic data acquisition systems. These data problems can result in large parameter variations on a flight to flight basis. The problem is exacerbated in many older data acquisition systems where a very limited amount of data is recorded per flight (with one data point per flight being the norm) and the data is often recorded under a fixed set of engine operating conditions.
The noisy and limited data sets can cause several complications for automated engine monitoring and diagnosis. First, many engine problems are not distinguishable or even visible within small sets of recorded parameters. Second, the standard deviation over the flight to flight data points for a normally operating engine may be larger than the distance between the mean of the data associated with the normal condition and the mean of the data associated with the faulty engine condition. Furthermore, having data recorded only under a specific set of operating conditions may result in large data gaps. This results in a compromised ability to detect faults in the turbine engine system.
Thus, what is needed is an improved system and method for detecting engine faults that occur in a wide variety of operating conditions, which can also consistently detect engine faults from limited and sometimes noisy engine data sets.